Abstract
Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than . However, the result obtained from the TLS were better than those obtained from the DC.
Highlights
The demand for structural steel has been increasing over time owing to its numerous benefits, such as high ductility and tensile strength, lighter weight relative to concrete, and ease of use in construction
The deflection of the beam was determined from the depth camera (DC) data using the following procedure:
Because the DC is very sensitive to inherent noise, noise should be treated prominently
Summary
The demand for structural steel has been increasing over time owing to its numerous benefits, such as high ductility and tensile strength, lighter weight relative to concrete, and ease of use in construction. Its performance may be affected by unintended excessive impact loads and environmental disasters. These and several other defects lead to unforeseen structural damages, resulting in major catastrophes. The sensor that is affixed to the structure should be inexpensive because economic considerations are an important aspect in the construction industry and should be optimized. Optical sensors, such as depth cameras (DCs), kinetic cameras, fiber optics sensors, and range cameras, are the most favorable inexpensive vision-based sensors and are effective in acquiring structural responses via images, videos, and spatial coordinates [2,3,4]
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